code: def _diagonal(self) -> torch.Tensor:
      # Getting the diagonal of a kernel can be handled more efficiently by
      # transposing the batch and data dimension before calling the kernel.
      # Implementing it this way allows us to compute predictions more efficiently
      # in cases where only the variances are required.
      from ..kernels import Kernel
  
      x1 = self.x1
      x2 = self.x2
  
      res = super(Kernel, self.kernel).__call__(
          x1,
          x2,
          diag=True,
          last_dim_is_batch=self.last_dim_is_batch,
          **self.params,
      )
  
      # Now we'll make sure that the shape we're getting from diag makes sense
      if settings.debug.on():
          expected_shape = self.shape[:-1]
          if res.shape != expected_shape:
              raise RuntimeError(
                  "The kernel {} is not equipped to handle and diag. Expected size {}. "
                  "Got size {}".format(
                      self.kernel.__class__.__name__,
                      expected_shape,
                      res.shape,
                  )
              )
  
      if isinstance(res, LinearOperator):
          res = res.to_dense()
      return res.view(self.shape[:-1]).contiguous()
  
imports: import functools
  from typing import Callable
  import torch
  from linear_operator import LinearOperator, to_linear_operator
  from linear_operator.utils.getitem import _noop_index
  from ..None import beta_features, settings
  from ..utils import deprecation
  from ..utils.memoize import cached
label_desc: Use image node_id 8 for calling the LazyEvaluatedKernelTensor obj's underlying member method code with example usage: obj._diagonal() and returns: res